Internet of Things (IOT) is a network of uniquely-identifiable and purposed “things” that are enabled to communicate data over a communications network without requiring human-to-human or human-to-computer interaction. The “thing” in the “Internet of Things” may virtually be anything that fits into a common purpose thereof. For example, a “thing” could be a person with a heart rate monitor implant, a farm animal with a biochip transponder, an automobile comprising built-in sensors configured to alert its driver when the tire pressure is low, or the like, or any other natural or man-made entity that can be assigned with a unique IP address and provided with the ability to transfer data over a network. Notably, if all the entities in an IOT are machines, then the IOT is referred to as a Machine to Machine (M2M) IOT or simply, as M2M IOT.
It is apparent from the aforementioned examples that an entity becomes a “thing” of an M2M IOT especially, when the entity is attached with one or more sensors capable of capturing one or more types of data pertaining to: segregation of the data (if applicable); selective communication of each segregation of data to one or more fellow “things”; reception of one or more control commands (or instructions) from one or more fellow “things” wherein, the control commands are based on the data received by the one or more fellow “things”; and execution of the commands resulting in manipulation or “management” of an operation of the corresponding entity. Therefore, in an IOT-enabled system, the “things” basically manage themselves without any human intervention, thus drastically improving the efficiency thereof.
In the prior art, some patents disclose systems and methods for predictive or preventive maintenance of machines based on sensor data analytics. For example, U.S. Pat. No. 8,571,904 B2 describes regarding optimization of control systems for networked industrial sensors and associated methods primarily in an industrial automation environment. The invention provides self-sensing and/or communication with sensors, and integration of control methods and strategies to optimize performance and operational objectives.
U.S. Pat. No. 8,126,574 B2 relates to control system and methods for selecting, controlling and optimizing utilization of machinery primarily in an industrial automation environment. The invention employs machine diagnostic and/or prognostic information for optimizing an overall business operation.
U.S. Pat. No. 7,406,399 B2 describes system and method for obtaining and analyzing data from one or more discrete machines, automatically determining relationships related to the data, taking corrective action to improve machine operation and/or maintenance, automatically and heuristically predicting a failure associated with the machine and/or recommending preventive maintenance in advance of the failure, and/or automating and analyzing mining shovels.
U.S. Pat. No. 8,112,381 B2 relates to improved techniques for early fault detection via condition-based maintenance (CBM) and predictive maintenance (PM) using a wireless sensor network to gather data from a large number of sensors.
WO 2014044906 A1 describes predictive maintenance method of hoisting equipment, particularly a crane, the method comprising the steps of: automatically collecting, at a maintenance center, diagnostic data relating to a component of a remote hoisting equipment and optionally sensor data relating the operational environment of the remote hoisting equipment; providing configuration data of the remote hoisting equipment; providing reliability data on said at least one component of the remote hoisting equipment; automatically generating, based on said diagnostic data, configuration data, reliability data and optionally on said operational environment data, a maintenance plan optimizing the cost of maintenance and reliability of the hoisting equipment over a life cycle of the hoisting equipment.
It is evident from the above discussion of the aforementioned prior art that none of them discloses or suggests determination of machine reliability based on a scalable IOT system. The aforementioned prior fail to show or suggest scalable tools for managing and analyzing the collected sensor data and mobile middleware solutions to shuffle and map the data for a Big Data layer. Therefore, there is a need in the art for a solution to the aforementioned problem which addresses the data volume and speed scalability along with a complexity of mapping the data for analytical purposes.